Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images
Fully convolutional network ensembles for white matter hyperintensities segmentation in MR images
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DOI：
10.1016/j.neuroimage.2018.07.005
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发表时间：
2018-12-01
期刊：
影响因子：
5.7
通讯作者：
Menze, Bjoern
中科院分区：
文献类型：
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作者：
Li, Hongwei;Jiang, Gongfa;Menze, Bjoern
白色物质高信号（WMH）常见于健康老年人的脑中，并且与各种神经系统和老年疾病相关。在本文中，我们提出了一项研究，使用深度全卷积网络和集成模型，使用流体衰减反演恢复（FLAIR）和T1磁共振（MR）扫描自动检测这种WMH。该算法在MICCAI 2017的WMH细分挑战赛中进行了评估并排名第一。在评估阶段，该算法的实施被提交给挑战组织者，然后他们在来自5台扫描仪的110个隐藏案例上独立测试。在hold-out测试数据集上获得的平均骰子得分、精确度和稳健Hausdorff距离分别为80%、84%和6.30 mm。这些都是在挑战中取得的最高成绩，表明所提出的方法是最先进的。详细描述和定量分析的关键组成部分的系统。此外，跨扫描仪评估的研究，讨论如何组合的模态影响系统的泛化能力。系统的适应性，不同的扫描仪和协议进行了研究。进一步的定量研究显示了集合规模的影响和集合模型的有效性。此外，我们的方法的软件和模型是公开的。所提出的系统的有效性和泛化能力显示其潜在的现实世界的临床实践。
White matter hyperintensities (WMH) are commonly found in the brains of healthy elderly individuals and have been associated with various neurological and geriatric disorders. In this paper, we present a study using deep fully convolutional network and ensemble models to automatically detect such WMH using fluid attenuation inversion recovery (FLAIR) and T1 magnetic resonance (MR) scans. The algorithm was evaluated and ranked 1st in the WMH Segmentation Challenge at MICCAI 2017. In the evaluation stage, the implementation of the algorithm was submitted to the challenge organizers, who then independently tested it on a hidden set of 110 cases from 5 scanners. Averaged dice score, precision and robust Hausdorff distance obtained on held-out test datasets were 80%, 84% and 6.30 mm respectively. These were the highest achieved in the challenge, suggesting the proposed method is the state-of-the-art. Detailed descriptions and quantitative analysis on key components of the system were provided. Furthermore, a study of cross-scanner evaluation is presented to discuss how the combination of modalities affect the generalization capability of the system. The adaptability of the system to different scanners and protocols is also investigated. A quantitative study is further presented to show the effect of ensemble size and the effectiveness of the ensemble model. Additionally, software and models of our method are made publicly available. The effectiveness and generalization capability of the proposed system show its potential for real-world clinical practice.